JMIR Publications

ABSTRACT

Background:

Digital mental health interventions (DHIs) to monitor and improve the health of people with severe mental health problems (SMI) show promise, but user engagement is variable, and integrated clinical use low.

Objective:

This prospectively registered systematic review (PROSPERO: CRD42021282871) examined barriers and facilitators of clinician and patient engagement with DHIs to inform implementation within real-world settings.

Methods:

A systematic search of seven databases identified empirical studies reporting qualitative/quantitative data about factors affecting staff/patient engagement with DHIs aiming to monitor/improve the mental/physical health of people with SMI. The Consolidated Framework for Implementation Research (CFIR) was used to synthesize barriers and facilitators data following a best-fit framework synthesis approach.

Results:

The review included 175 papers (150 studies; 11,446 participants) describing RCTs, surveys, qualitative interviews, usability, cohort, and case studies. Samples included people with schizophrenia spectrum disorders (n=98 studies), bipolar disorder (n=62), and/or clinicians (n=26). Key facilitators were a strong recognition of DHIs’ relative advantages, a clear link between intervention focus and specific patient needs, simple/low-effort digital interface, human-supported delivery, and device provision where needed. Although staff thought patients would lose/damage/sell devices, reviewed studies found only 11% device loss. Barriers included intervention complexity, perceived risks, user motivation, discomfort with self-reflection, digital poverty, symptoms of psychosis, poor compatibility with existing clinical workflows, staff/patient fears that DHIs would replace traditional face-to-face care, infrastructure limitations, and limited financial support for delivery.

Conclusions:

Identified barriers and facilitators highlight key considerations for DHI development and implementation. As to broader implications, sustainable business models are needed to ensure that evidence-based DHIs are maintained and deployed.